Overview

Brought to you by YData

Dataset statistics

Number of variables19
Number of observations544025
Missing cells4665849
Missing cells (%)45.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory187.8 MiB
Average record size in memory362.0 B

Variable types

Categorical2
Numeric13
DateTime3
Text1

Alerts

month has constant value "1.0"Constant
Stunde des Tages (Ortszeit) is highly overall correlated with hour_localHigh correlation
hour_local is highly overall correlated with Stunde des Tages (Ortszeit)High correlation
qkfz is highly overall correlated with qlkw and 1 other fieldsHigh correlation
qlkw is highly overall correlated with qkfzHigh correlation
qpkw is highly overall correlated with qkfzHigh correlation
vkfz is highly overall correlated with vlkw and 1 other fieldsHigh correlation
vlkw is highly overall correlated with vkfz and 1 other fieldsHigh correlation
vpkw is highly overall correlated with vkfz and 1 other fieldsHigh correlation
Vollständigkeit has 354712 (65.2%) missing valuesMissing
ZScore_Det0 has 358781 (65.9%) missing valuesMissing
ZScore_Det1 has 356734 (65.6%) missing valuesMissing
ZScore_Det2 has 356655 (65.6%) missing valuesMissing
hist_cor has 355832 (65.4%) missing valuesMissing
localTime has 354712 (65.2%) missing valuesMissing
month has 354712 (65.2%) missing valuesMissing
qkfz has 354712 (65.2%) missing valuesMissing
qlkw has 354712 (65.2%) missing valuesMissing
qpkw has 354712 (65.2%) missing valuesMissing
vkfz has 359496 (66.1%) missing valuesMissing
vlkw has 389818 (71.7%) missing valuesMissing
vpkw has 360261 (66.2%) missing valuesMissing
Stunde des Tages (Ortszeit) has 22614 (4.2%) zerosZeros
ZScore_Det0 has 80140 (14.7%) zerosZeros
ZScore_Det1 has 81256 (14.9%) zerosZeros
ZScore_Det2 has 156995 (28.9%) zerosZeros
qlkw has 35106 (6.5%) zerosZeros
qpkw has 5549 (1.0%) zerosZeros
hour_local has 22614 (4.2%) zerosZeros

Reproduction

Analysis started2025-11-19 10:08:32.876908
Analysis finished2025-11-19 10:09:41.489241
Duration1 minute and 8.61 seconds
Software versionydata-profiling vv4.17.0
Download configurationconfig.json

Variables

Datum (Ortszeit)
Categorical

Distinct31
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size30.6 MiB
2024-01-26
 
17981
2024-01-20
 
17857
2024-01-15
 
17830
2024-01-31
 
17819
2024-01-21
 
17805
Other values (26)
454733 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters5440250
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2024-01-01
2nd row2024-01-01
3rd row2024-01-01
4th row2024-01-01
5th row2024-01-01

Common Values

ValueCountFrequency (%)
2024-01-2617981
 
3.3%
2024-01-2017857
 
3.3%
2024-01-1517830
 
3.3%
2024-01-3117819
 
3.3%
2024-01-2117805
 
3.3%
2024-01-2217785
 
3.3%
2024-01-0217756
 
3.3%
2024-01-1817737
 
3.3%
2024-01-2417726
 
3.3%
2024-01-3017722
 
3.3%
Other values (21)366007
67.3%

Length

2025-11-19T11:09:41.637236image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2024-01-2617981
 
3.3%
2024-01-2017857
 
3.3%
2024-01-1517830
 
3.3%
2024-01-3117819
 
3.3%
2024-01-2117805
 
3.3%
2024-01-2217785
 
3.3%
2024-01-0217756
 
3.3%
2024-01-1817737
 
3.3%
2024-01-2417726
 
3.3%
2024-01-3017722
 
3.3%
Other values (21)366007
67.3%

Most occurring characters

ValueCountFrequency (%)
21318085
24.2%
01297351
23.8%
-1088050
20.0%
1790291
14.5%
4596721
11.0%
388515
 
1.6%
552683
 
1.0%
952551
 
1.0%
652401
 
1.0%
852132
 
1.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)5440250
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
21318085
24.2%
01297351
23.8%
-1088050
20.0%
1790291
14.5%
4596721
11.0%
388515
 
1.6%
552683
 
1.0%
952551
 
1.0%
652401
 
1.0%
852132
 
1.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)5440250
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
21318085
24.2%
01297351
23.8%
-1088050
20.0%
1790291
14.5%
4596721
11.0%
388515
 
1.6%
552683
 
1.0%
952551
 
1.0%
652401
 
1.0%
852132
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)5440250
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
21318085
24.2%
01297351
23.8%
-1088050
20.0%
1790291
14.5%
4596721
11.0%
388515
 
1.6%
552683
 
1.0%
952551
 
1.0%
652401
 
1.0%
852132
 
1.0%

Stunde des Tages (Ortszeit)
Real number (ℝ)

High correlation  Zeros 

Distinct24
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.388157
Minimum0
Maximum23
Zeros22614
Zeros (%)4.2%
Negative0
Negative (%)0.0%
Memory size4.2 MiB
2025-11-19T11:09:41.859248image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q15
median11
Q317
95-th percentile22
Maximum23
Range23
Interquartile range (IQR)12

Descriptive statistics

Standard deviation6.8450125
Coefficient of variation (CV)0.60106412
Kurtosis-1.1977
Mean11.388157
Median Absolute Deviation (MAD)6
Skewness-6.2380842 × 10-5
Sum6195442
Variance46.854196
MonotonicityNot monotonic
2025-11-19T11:09:42.069887image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
1623217
 
4.3%
1223213
 
4.3%
823005
 
4.2%
1522967
 
4.2%
1122965
 
4.2%
1322953
 
4.2%
2022951
 
4.2%
1822951
 
4.2%
1722949
 
4.2%
2122947
 
4.2%
Other values (14)313907
57.7%
ValueCountFrequency (%)
022614
4.2%
122873
4.2%
222874
4.2%
322855
4.2%
422855
4.2%
522853
4.2%
622832
4.2%
722800
4.2%
823005
4.2%
922842
4.2%
ValueCountFrequency (%)
2316778
3.1%
2222931
4.2%
2122947
4.2%
2022951
4.2%
1922946
4.2%
1822951
4.2%
1722949
4.2%
1623217
4.3%
1522967
4.2%
1422944
4.2%

Vollständigkeit
Real number (ℝ)

Missing 

Distinct13
Distinct (%)< 0.1%
Missing354712
Missing (%)65.2%
Infinite0
Infinite (%)0.0%
Mean96.550998
Minimum8
Maximum108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.2 MiB
2025-11-19T11:09:42.282788image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum8
5-th percentile75
Q1100
median100
Q3100
95-th percentile100
Maximum108
Range100
Interquartile range (IQR)0

Descriptive statistics

Standard deviation11.717356
Coefficient of variation (CV)0.12135924
Kurtosis29.076948
Mean96.550998
Median Absolute Deviation (MAD)0
Skewness-5.0505301
Sum18278359
Variance137.29643
MonotonicityNot monotonic
2025-11-19T11:09:42.491849image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
100159876
29.4%
9213948
 
2.6%
835692
 
1.0%
753753
 
0.7%
671703
 
0.3%
8947
 
0.2%
58826
 
0.2%
17609
 
0.1%
25514
 
0.1%
42485
 
0.1%
Other values (3)960
 
0.2%
(Missing)354712
65.2%
ValueCountFrequency (%)
8947
 
0.2%
17609
 
0.1%
25514
 
0.1%
33470
 
0.1%
42485
 
0.1%
50482
 
0.1%
58826
 
0.2%
671703
 
0.3%
753753
0.7%
835692
1.0%
ValueCountFrequency (%)
1088
 
< 0.1%
100159876
29.4%
9213948
 
2.6%
835692
 
1.0%
753753
 
0.7%
671703
 
0.3%
58826
 
0.2%
50482
 
0.1%
42485
 
0.1%
33470
 
0.1%

ZScore_Det0
Real number (ℝ)

Missing  Zeros 

Distinct570
Distinct (%)0.3%
Missing358781
Missing (%)65.9%
Infinite0
Infinite (%)0.0%
Mean-0.1939909
Minimum-34.87
Maximum4.21
Zeros80140
Zeros (%)14.7%
Negative51518
Negative (%)9.5%
Memory size4.2 MiB
2025-11-19T11:09:42.797907image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum-34.87
5-th percentile-2.42
Q1-0.15
median0
Q30.18
95-th percentile0.88
Maximum4.21
Range39.08
Interquartile range (IQR)0.33

Descriptive statistics

Standard deviation0.92482634
Coefficient of variation (CV)-4.7673697
Kurtosis16.286641
Mean-0.1939909
Median Absolute Deviation (MAD)0.17
Skewness-2.043455
Sum-35935.65
Variance0.85530375
MonotonicityNot monotonic
2025-11-19T11:09:43.147142image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
080140
 
14.7%
-1.381050
 
0.2%
-1.31902
 
0.2%
0.48875
 
0.2%
0.58866
 
0.2%
0.49823
 
0.2%
0.42791
 
0.1%
0.39775
 
0.1%
-1.41767
 
0.1%
0.46743
 
0.1%
Other values (560)97512
 
17.9%
(Missing)358781
65.9%
ValueCountFrequency (%)
-34.871
 
< 0.1%
-15.541
 
< 0.1%
-11.152
 
< 0.1%
-10.861
 
< 0.1%
-9.841
 
< 0.1%
-9.764
 
< 0.1%
-9.323
 
< 0.1%
-8.082
 
< 0.1%
-7.5924
< 0.1%
-7.415
 
< 0.1%
ValueCountFrequency (%)
4.2124
< 0.1%
3.9724
< 0.1%
3.629
 
< 0.1%
3.451
 
< 0.1%
3.124
< 0.1%
2.4824
< 0.1%
2.3124
< 0.1%
2.2624
< 0.1%
2.248
 
< 0.1%
2.1724
< 0.1%

ZScore_Det1
Real number (ℝ)

Missing  Zeros 

Distinct566
Distinct (%)0.3%
Missing356734
Missing (%)65.6%
Infinite0
Infinite (%)0.0%
Mean-0.20558329
Minimum-34.87
Maximum4.34
Zeros81256
Zeros (%)14.9%
Negative51865
Negative (%)9.5%
Memory size4.2 MiB
2025-11-19T11:09:43.491190image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum-34.87
5-th percentile-2.38
Q1-0.14
median0
Q30.17
95-th percentile0.85
Maximum4.34
Range39.21
Interquartile range (IQR)0.31

Descriptive statistics

Standard deviation0.92613423
Coefficient of variation (CV)-4.50491
Kurtosis16.970601
Mean-0.20558329
Median Absolute Deviation (MAD)0.16
Skewness-2.1330175
Sum-38503.9
Variance0.85772461
MonotonicityNot monotonic
2025-11-19T11:09:43.837794image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
081256
 
14.9%
-1.431168
 
0.2%
-1.271058
 
0.2%
0.48902
 
0.2%
0.44825
 
0.2%
0.3802
 
0.1%
0.39787
 
0.1%
0.42766
 
0.1%
0.66765
 
0.1%
0.47765
 
0.1%
Other values (556)98197
 
18.1%
(Missing)356734
65.6%
ValueCountFrequency (%)
-34.871
 
< 0.1%
-15.541
 
< 0.1%
-15.411
 
< 0.1%
-11.955
 
< 0.1%
-11.152
 
< 0.1%
-9.841
 
< 0.1%
-9.764
 
< 0.1%
-9.313
 
< 0.1%
-8.082
 
< 0.1%
-7.5824
< 0.1%
ValueCountFrequency (%)
4.3424
< 0.1%
3.4124
< 0.1%
3.39
 
< 0.1%
3.068
 
< 0.1%
2.9724
< 0.1%
2.7324
< 0.1%
2.5324
< 0.1%
2.3924
< 0.1%
2.3224
< 0.1%
2.0524
< 0.1%

ZScore_Det2
Real number (ℝ)

Missing  Zeros 

Distinct414
Distinct (%)0.2%
Missing356655
Missing (%)65.6%
Infinite0
Infinite (%)0.0%
Mean-0.03569691
Minimum-15.41
Maximum4.12
Zeros156995
Zeros (%)28.9%
Negative13232
Negative (%)2.4%
Memory size4.2 MiB
2025-11-19T11:09:44.221352image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum-15.41
5-th percentile-0.5
Q10
median0
Q30
95-th percentile0.5
Maximum4.12
Range19.53
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.49643542
Coefficient of variation (CV)-13.906958
Kurtosis41.397772
Mean-0.03569691
Median Absolute Deviation (MAD)0
Skewness-4.1261846
Sum-6688.53
Variance0.24644813
MonotonicityNot monotonic
2025-11-19T11:09:44.583381image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0156995
28.9%
0.55330
 
0.1%
0.52311
 
0.1%
0.38310
 
0.1%
0.58309
 
0.1%
0.47287
 
0.1%
0.59275
 
0.1%
0.5264
 
< 0.1%
0.4264
 
< 0.1%
0.8263
 
< 0.1%
Other values (404)27762
 
5.1%
(Missing)356655
65.6%
ValueCountFrequency (%)
-15.411
 
< 0.1%
-11.955
 
< 0.1%
-6.995
 
< 0.1%
-6.88
 
< 0.1%
-6.1124
< 0.1%
-6.14
 
< 0.1%
-6.0314
< 0.1%
-5.953
 
< 0.1%
-5.683
 
< 0.1%
-5.511
 
< 0.1%
ValueCountFrequency (%)
4.1224
< 0.1%
3.451
 
< 0.1%
3.1924
< 0.1%
3.0923
< 0.1%
3.058
 
< 0.1%
2.7724
< 0.1%
2.3624
< 0.1%
2.248
 
< 0.1%
2.1524
< 0.1%
2.0424
< 0.1%

hist_cor
Real number (ℝ)

Missing 

Distinct164
Distinct (%)0.1%
Missing355832
Missing (%)65.4%
Infinite0
Infinite (%)0.0%
Mean0.77237607
Minimum-1
Maximum1
Zeros214
Zeros (%)< 0.1%
Negative4597
Negative (%)0.8%
Memory size4.2 MiB
2025-11-19T11:09:44.953671image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile0.18
Q10.76
median0.86
Q30.91
95-th percentile0.94
Maximum1
Range2
Interquartile range (IQR)0.15

Descriptive statistics

Standard deviation0.243079
Coefficient of variation (CV)0.31471586
Kurtosis6.9606126
Mean0.77237607
Median Absolute Deviation (MAD)0.06
Skewness-2.5441274
Sum145355.77
Variance0.059087402
MonotonicityNot monotonic
2025-11-19T11:09:45.333819image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.9113717
 
2.5%
0.912577
 
2.3%
0.9211624
 
2.1%
0.9310904
 
2.0%
0.8910861
 
2.0%
0.879283
 
1.7%
0.949047
 
1.7%
0.889033
 
1.7%
0.867940
 
1.5%
0.855842
 
1.1%
Other values (154)87365
 
16.1%
(Missing)355832
65.4%
ValueCountFrequency (%)
-13
 
< 0.1%
-0.982
 
< 0.1%
-0.972
 
< 0.1%
-0.841
 
< 0.1%
-0.831
 
< 0.1%
-0.824
< 0.1%
-0.7624
< 0.1%
-0.6925
< 0.1%
-0.679
 
< 0.1%
-0.6423
< 0.1%
ValueCountFrequency (%)
13
 
< 0.1%
0.981
 
< 0.1%
0.97123
 
< 0.1%
0.961527
 
0.3%
0.955330
 
1.0%
0.949047
1.7%
0.9310904
2.0%
0.9211624
2.1%
0.9113717
2.5%
0.912577
2.3%

localTime
Date

Missing 

Distinct743
Distinct (%)0.4%
Missing354712
Missing (%)65.2%
Memory size4.2 MiB
Minimum2024-01-01 01:00:00+01:00
Maximum2024-01-31 23:00:00+01:00
Invalid dates0
Invalid dates (%)0.0%
2025-11-19T11:09:45.725880image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-11-19T11:09:46.037048image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

month
Categorical

Constant  Missing 

Distinct1
Distinct (%)< 0.1%
Missing354712
Missing (%)65.2%
Memory size28.3 MiB
1.0
189313 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters567939
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0189313
34.8%
(Missing)354712
65.2%

Length

2025-11-19T11:09:46.310073image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-19T11:09:46.506050image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
1.0189313
100.0%

Most occurring characters

ValueCountFrequency (%)
1189313
33.3%
.189313
33.3%
0189313
33.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)567939
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1189313
33.3%
.189313
33.3%
0189313
33.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)567939
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1189313
33.3%
.189313
33.3%
0189313
33.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)567939
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1189313
33.3%
.189313
33.3%
0189313
33.3%

qkfz
Real number (ℝ)

High correlation  Missing 

Distinct1281
Distinct (%)0.7%
Missing354712
Missing (%)65.2%
Infinite0
Infinite (%)0.0%
Mean235.54997
Minimum0
Maximum3708
Zeros4784
Zeros (%)0.9%
Negative0
Negative (%)0.0%
Memory size4.2 MiB
2025-11-19T11:09:47.037659image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile6
Q160
median187
Q3367
95-th percentile618
Maximum3708
Range3708
Interquartile range (IQR)307

Descriptive statistics

Standard deviation205.49165
Coefficient of variation (CV)0.87239087
Kurtosis1.7295734
Mean235.54997
Median Absolute Deviation (MAD)142
Skewness1.0820297
Sum44592672
Variance42226.817
MonotonicityNot monotonic
2025-11-19T11:09:47.322611image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
04784
 
0.9%
1987
 
0.2%
24918
 
0.2%
22854
 
0.2%
4840
 
0.2%
12824
 
0.2%
7824
 
0.2%
20815
 
0.1%
8815
 
0.1%
2811
 
0.1%
Other values (1271)176841
32.5%
(Missing)354712
65.2%
ValueCountFrequency (%)
04784
0.9%
1987
 
0.2%
2811
 
0.1%
3758
 
0.1%
4840
 
0.2%
5781
 
0.1%
6747
 
0.1%
7824
 
0.2%
8815
 
0.1%
9768
 
0.1%
ValueCountFrequency (%)
37081
< 0.1%
23681
< 0.1%
22771
< 0.1%
20811
< 0.1%
19251
< 0.1%
18401
< 0.1%
18161
< 0.1%
18071
< 0.1%
17741
< 0.1%
17531
< 0.1%

qlkw
Real number (ℝ)

High correlation  Missing  Zeros 

Distinct605
Distinct (%)0.3%
Missing354712
Missing (%)65.2%
Infinite0
Infinite (%)0.0%
Mean16.895559
Minimum0
Maximum1356
Zeros35106
Zeros (%)6.5%
Negative0
Negative (%)0.0%
Memory size4.2 MiB
2025-11-19T11:09:47.600691image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median5
Q313
95-th percentile64
Maximum1356
Range1356
Interquartile range (IQR)12

Descriptive statistics

Standard deviation45.577299
Coefficient of variation (CV)2.6975904
Kurtosis59.695335
Mean16.895559
Median Absolute Deviation (MAD)5
Skewness6.6284161
Sum3198549
Variance2077.2902
MonotonicityNot monotonic
2025-11-19T11:09:47.913694image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
035106
 
6.5%
118060
 
3.3%
215345
 
2.8%
312818
 
2.4%
410843
 
2.0%
59103
 
1.7%
67463
 
1.4%
77066
 
1.3%
86112
 
1.1%
95147
 
0.9%
Other values (595)62250
 
11.4%
(Missing)354712
65.2%
ValueCountFrequency (%)
035106
6.5%
118060
3.3%
215345
2.8%
312818
 
2.4%
410843
 
2.0%
59103
 
1.7%
67463
 
1.4%
77066
 
1.3%
86112
 
1.1%
95147
 
0.9%
ValueCountFrequency (%)
13561
< 0.1%
9451
< 0.1%
8981
< 0.1%
8841
< 0.1%
8651
< 0.1%
8621
< 0.1%
7941
< 0.1%
7891
< 0.1%
7881
< 0.1%
7801
< 0.1%

qpkw
Real number (ℝ)

High correlation  Missing  Zeros 

Distinct1230
Distinct (%)0.6%
Missing354712
Missing (%)65.2%
Infinite0
Infinite (%)0.0%
Mean218.64994
Minimum0
Maximum2352
Zeros5549
Zeros (%)1.0%
Negative0
Negative (%)0.0%
Memory size4.2 MiB
2025-11-19T11:09:48.197737image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4
Q151
median168
Q3344
95-th percentile588
Maximum2352
Range2352
Interquartile range (IQR)293

Descriptive statistics

Standard deviation196.79911
Coefficient of variation (CV)0.90006477
Kurtosis1.3317524
Mean218.64994
Median Absolute Deviation (MAD)133
Skewness1.0918288
Sum41393276
Variance38729.888
MonotonicityNot monotonic
2025-11-19T11:09:48.443745image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
05549
 
1.0%
11353
 
0.2%
21118
 
0.2%
121038
 
0.2%
41030
 
0.2%
5992
 
0.2%
3987
 
0.2%
14970
 
0.2%
8958
 
0.2%
16945
 
0.2%
Other values (1220)174373
32.1%
(Missing)354712
65.2%
ValueCountFrequency (%)
05549
1.0%
11353
 
0.2%
21118
 
0.2%
3987
 
0.2%
41030
 
0.2%
5992
 
0.2%
6875
 
0.2%
7922
 
0.2%
8958
 
0.2%
9896
 
0.2%
ValueCountFrequency (%)
23521
< 0.1%
19241
< 0.1%
18241
< 0.1%
17991
< 0.1%
17741
< 0.1%
17411
< 0.1%
17311
< 0.1%
16901
< 0.1%
16761
< 0.1%
16551
< 0.1%

utc
Date

Distinct744
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size4.2 MiB
Minimum2023-12-31 23:00:00+00:00
Maximum2024-01-31 22:00:00+00:00
Invalid dates0
Invalid dates (%)0.0%
2025-11-19T11:09:48.714205image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-11-19T11:09:49.033206image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

vkfz
Real number (ℝ)

High correlation  Missing 

Distinct101
Distinct (%)0.1%
Missing359496
Missing (%)66.1%
Infinite0
Infinite (%)0.0%
Mean45.406456
Minimum-1
Maximum100
Zeros0
Zeros (%)0.0%
Negative42
Negative (%)< 0.1%
Memory size4.2 MiB
2025-11-19T11:09:49.296120image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile24
Q139
median46
Q352
95-th percentile62
Maximum100
Range101
Interquartile range (IQR)13

Descriptive statistics

Standard deviation12.674587
Coefficient of variation (CV)0.27913622
Kurtosis2.8452344
Mean45.406456
Median Absolute Deviation (MAD)6
Skewness-0.32737034
Sum8378808
Variance160.64515
MonotonicityNot monotonic
2025-11-19T11:09:49.565715image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
488055
 
1.5%
478032
 
1.5%
467997
 
1.5%
497806
 
1.4%
457553
 
1.4%
507546
 
1.4%
447450
 
1.4%
516956
 
1.3%
436914
 
1.3%
526645
 
1.2%
Other values (91)109575
 
20.1%
(Missing)359496
66.1%
ValueCountFrequency (%)
-142
 
< 0.1%
155
 
< 0.1%
22
 
< 0.1%
32819
0.5%
4206
 
< 0.1%
5157
 
< 0.1%
6119
 
< 0.1%
7126
 
< 0.1%
8125
 
< 0.1%
9153
 
< 0.1%
ValueCountFrequency (%)
10086
 
< 0.1%
99125
< 0.1%
9840
 
< 0.1%
9746
 
< 0.1%
9684
 
< 0.1%
9597
 
< 0.1%
94106
< 0.1%
93139
< 0.1%
92154
< 0.1%
91246
< 0.1%

vlkw
Real number (ℝ)

High correlation  Missing 

Distinct100
Distinct (%)0.1%
Missing389818
Missing (%)71.7%
Infinite0
Infinite (%)0.0%
Mean46.187618
Minimum-1
Maximum100
Zeros0
Zeros (%)0.0%
Negative39
Negative (%)< 0.1%
Memory size4.2 MiB
2025-11-19T11:09:49.855979image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile28
Q140
median46
Q352
95-th percentile64
Maximum100
Range101
Interquartile range (IQR)12

Descriptive statistics

Standard deviation11.954207
Coefficient of variation (CV)0.25881845
Kurtosis2.7901961
Mean46.187618
Median Absolute Deviation (MAD)6
Skewness0.31688122
Sum7122454
Variance142.90308
MonotonicityNot monotonic
2025-11-19T11:09:50.144936image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
487240
 
1.3%
467117
 
1.3%
446842
 
1.3%
506469
 
1.2%
476144
 
1.1%
426054
 
1.1%
495846
 
1.1%
455828
 
1.1%
525825
 
1.1%
435510
 
1.0%
Other values (90)91332
 
16.8%
(Missing)389818
71.7%
ValueCountFrequency (%)
-139
 
< 0.1%
149
 
< 0.1%
3744
0.1%
431
 
< 0.1%
517
 
< 0.1%
633
 
< 0.1%
737
 
< 0.1%
843
 
< 0.1%
973
 
< 0.1%
1064
 
< 0.1%
ValueCountFrequency (%)
10085
 
< 0.1%
99117
< 0.1%
9838
 
< 0.1%
9735
 
< 0.1%
9673
 
< 0.1%
9598
 
< 0.1%
94109
< 0.1%
93134
< 0.1%
92188
< 0.1%
91269
< 0.1%

vpkw
Real number (ℝ)

High correlation  Missing 

Distinct101
Distinct (%)0.1%
Missing360261
Missing (%)66.2%
Infinite0
Infinite (%)0.0%
Mean45.306551
Minimum-1
Maximum100
Zeros0
Zeros (%)0.0%
Negative8
Negative (%)< 0.1%
Memory size4.2 MiB
2025-11-19T11:09:50.428000image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile24
Q139
median46
Q352
95-th percentile62
Maximum100
Range101
Interquartile range (IQR)13

Descriptive statistics

Standard deviation12.556392
Coefficient of variation (CV)0.27714296
Kurtosis2.6614745
Mean45.306551
Median Absolute Deviation (MAD)6
Skewness-0.40793161
Sum8325713
Variance157.66297
MonotonicityNot monotonic
2025-11-19T11:09:50.722433image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
487998
 
1.5%
477969
 
1.5%
467954
 
1.5%
497754
 
1.4%
507589
 
1.4%
457509
 
1.4%
447467
 
1.4%
516936
 
1.3%
436882
 
1.3%
526635
 
1.2%
Other values (91)109071
 
20.0%
(Missing)360261
66.2%
ValueCountFrequency (%)
-18
 
< 0.1%
111
 
< 0.1%
22
 
< 0.1%
32789
0.5%
4230
 
< 0.1%
5164
 
< 0.1%
6129
 
< 0.1%
7137
 
< 0.1%
8126
 
< 0.1%
9162
 
< 0.1%
ValueCountFrequency (%)
10013
 
< 0.1%
9937
 
< 0.1%
9842
 
< 0.1%
9725
 
< 0.1%
9663
 
< 0.1%
9592
 
< 0.1%
9490
 
< 0.1%
93138
< 0.1%
92195
< 0.1%
91286
0.1%
Distinct485
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size32.4 MiB
2025-11-19T11:09:51.103588image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length23
Median length13
Mean length13.363255
Min length13

Characters and Unicode

Total characters7269945
Distinct characters23
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTEU00002_Det0
2nd rowTEU00002_Det0
3rd rowTEU00002_Det0
4th rowTEU00002_Det0
5th rowTEU00002_Det0
ValueCountFrequency (%)
teu00002_det01487
 
0.3%
teu00320_det11487
 
0.3%
teu00424_det01487
 
0.3%
teu00424_det11487
 
0.3%
teu00425_det01487
 
0.3%
teu00211_det11487
 
0.3%
teu00211_det01487
 
0.3%
teu00208_det11487
 
0.3%
teu00208_det01487
 
0.3%
teu00248_det11487
 
0.3%
Other values (475)529155
97.3%
2025-11-19T11:09:51.724968image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
01647620
22.7%
D583549
 
8.0%
e544025
 
7.5%
t544025
 
7.5%
T524263
 
7.2%
U524263
 
7.2%
_524263
 
7.2%
E524263
 
7.2%
1459065
 
6.3%
2271463
 
3.7%
Other values (13)1123146
15.4%

Most occurring categories

ValueCountFrequency (%)
(unknown)7269945
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
01647620
22.7%
D583549
 
8.0%
e544025
 
7.5%
t544025
 
7.5%
T524263
 
7.2%
U524263
 
7.2%
_524263
 
7.2%
E524263
 
7.2%
1459065
 
6.3%
2271463
 
3.7%
Other values (13)1123146
15.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown)7269945
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
01647620
22.7%
D583549
 
8.0%
e544025
 
7.5%
t544025
 
7.5%
T524263
 
7.2%
U524263
 
7.2%
_524263
 
7.2%
E524263
 
7.2%
1459065
 
6.3%
2271463
 
3.7%
Other values (13)1123146
15.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown)7269945
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
01647620
22.7%
D583549
 
8.0%
e544025
 
7.5%
t544025
 
7.5%
T524263
 
7.2%
U524263
 
7.2%
_524263
 
7.2%
E524263
 
7.2%
1459065
 
6.3%
2271463
 
3.7%
Other values (13)1123146
15.4%
Distinct31
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.2 MiB
Minimum2024-01-01 00:00:00
Maximum2024-01-31 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-11-19T11:09:51.995199image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-11-19T11:09:52.328165image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=31)

hour_local
Real number (ℝ)

High correlation  Zeros 

Distinct24
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.388157
Minimum0
Maximum23
Zeros22614
Zeros (%)4.2%
Negative0
Negative (%)0.0%
Memory size4.2 MiB
2025-11-19T11:09:52.686386image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q15
median11
Q317
95-th percentile22
Maximum23
Range23
Interquartile range (IQR)12

Descriptive statistics

Standard deviation6.8450125
Coefficient of variation (CV)0.60106412
Kurtosis-1.1977
Mean11.388157
Median Absolute Deviation (MAD)6
Skewness-6.2380842 × 10-5
Sum6195442
Variance46.854196
MonotonicityNot monotonic
2025-11-19T11:09:52.914397image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
1623217
 
4.3%
1223213
 
4.3%
823005
 
4.2%
1522967
 
4.2%
1122965
 
4.2%
1322953
 
4.2%
2022951
 
4.2%
1822951
 
4.2%
1722949
 
4.2%
2122947
 
4.2%
Other values (14)313907
57.7%
ValueCountFrequency (%)
022614
4.2%
122873
4.2%
222874
4.2%
322855
4.2%
422855
4.2%
522853
4.2%
622832
4.2%
722800
4.2%
823005
4.2%
922842
4.2%
ValueCountFrequency (%)
2316778
3.1%
2222931
4.2%
2122947
4.2%
2022951
4.2%
1922946
4.2%
1822951
4.2%
1722949
4.2%
1623217
4.3%
1522967
4.2%
1422944
4.2%

Interactions

2025-11-19T11:09:33.578569image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-11-19T11:08:53.205920image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-11-19T11:08:56.524868image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-11-19T11:08:59.634696image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-11-19T11:09:02.842858image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-11-19T11:09:06.437391image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-11-19T11:09:09.904422image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-11-19T11:09:13.587634image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-11-19T11:09:17.139330image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-11-19T11:09:20.628348image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-11-19T11:09:23.683959image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-11-19T11:09:26.885986image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-11-19T11:09:30.181069image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-11-19T11:09:33.782910image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-11-19T11:08:53.430479image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-11-19T11:08:56.761021image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-11-19T11:08:59.875240image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-11-19T11:09:03.079920image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-11-19T11:09:06.690389image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-11-19T11:09:10.162647image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-11-19T11:09:13.902164image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-11-19T11:09:17.365344image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-11-19T11:09:20.887815image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-11-19T11:09:23.950680image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-11-19T11:09:27.149953image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-11-19T11:09:30.395220image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-11-19T11:09:34.008059image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-11-19T11:08:53.644082image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-11-19T11:08:56.991397image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-11-19T11:09:00.118242image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-11-19T11:09:03.336361image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-11-19T11:09:06.932390image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-11-19T11:09:10.443176image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-11-19T11:09:14.199142image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-11-19T11:09:17.611027image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-11-19T11:09:21.068126image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-11-19T11:09:24.223591image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-11-19T11:09:27.389328image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-11-19T11:09:30.616825image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-11-19T11:09:34.257418image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-11-19T11:08:53.905445image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-11-19T11:08:57.251744image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-11-19T11:09:00.390712image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-11-19T11:09:03.587824image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-11-19T11:09:07.197391image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-11-19T11:09:10.740343image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-11-19T11:09:14.512990image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-11-19T11:09:17.862637image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-11-19T11:09:21.320323image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-11-19T11:09:24.491662image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-11-19T11:09:27.654343image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-11-19T11:09:30.878574image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-11-19T11:09:34.506419image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-11-19T11:08:54.173694image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-11-19T11:08:57.521914image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-11-19T11:09:00.640443image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-11-19T11:09:03.873067image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-11-19T11:09:07.488391image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-11-19T11:09:11.054970image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-11-19T11:09:14.814129image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-11-19T11:09:18.423202image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-11-19T11:09:21.573766image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-11-19T11:09:24.737873image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-11-19T11:09:27.921355image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-11-19T11:09:31.124310image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-11-19T11:09:34.757418image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-11-19T11:08:54.432551image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-11-19T11:08:57.822767image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-11-19T11:09:00.905472image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-11-19T11:09:04.402059image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-11-19T11:09:07.750392image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-11-19T11:09:11.346606image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-11-19T11:09:15.102071image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-11-19T11:09:18.712651image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-11-19T11:09:21.826719image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-11-19T11:09:25.014872image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-11-19T11:09:28.199748image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-11-19T11:09:31.375309image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-11-19T11:09:35.011664image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-11-19T11:08:54.721775image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-11-19T11:08:58.076803image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-11-19T11:09:01.173995image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-11-19T11:09:04.671057image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-11-19T11:09:08.022642image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-11-19T11:09:11.648341image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-11-19T11:09:15.395128image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-11-19T11:09:18.981285image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-11-19T11:09:22.078733image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-11-19T11:09:25.273113image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-11-19T11:09:28.477433image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-11-19T11:09:31.635323image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-11-19T11:09:35.247680image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-11-19T11:08:54.986362image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-11-19T11:08:58.317864image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-11-19T11:09:01.429246image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-11-19T11:09:04.946846image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-11-19T11:09:08.308642image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-11-19T11:09:11.944348image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-11-19T11:09:15.654979image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-11-19T11:09:19.243308image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-11-19T11:09:22.314722image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-11-19T11:09:25.507413image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-11-19T11:09:28.746181image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-11-19T11:09:31.879323image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-11-19T11:09:35.486668image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-11-19T11:08:55.251548image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-11-19T11:08:58.560808image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-11-19T11:09:01.674443image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-11-19T11:09:05.195369image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-11-19T11:09:08.603025image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-11-19T11:09:12.249279image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-11-19T11:09:15.914123image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-11-19T11:09:19.496927image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-11-19T11:09:22.558814image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-11-19T11:09:25.750897image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-11-19T11:09:29.000723image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-11-19T11:09:32.151322image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-11-19T11:09:35.706667image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-11-19T11:08:55.477666image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-11-19T11:08:58.785196image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-11-19T11:09:01.911443image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-11-19T11:09:05.455258image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-11-19T11:09:08.871034image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-11-19T11:09:12.518281image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-11-19T11:09:16.164621image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-11-19T11:09:19.714752image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-11-19T11:09:22.765463image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-11-19T11:09:25.968031image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-11-19T11:09:29.224274image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-11-19T11:09:32.379307image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-11-19T11:09:35.921901image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-11-19T11:08:55.710209image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-11-19T11:08:58.995193image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-11-19T11:09:02.145939image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-11-19T11:09:05.665397image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-11-19T11:09:09.123312image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-11-19T11:09:12.792761image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-11-19T11:09:16.420993image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-11-19T11:09:19.942577image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-11-19T11:09:22.980143image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-11-19T11:09:26.175248image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-11-19T11:09:29.459784image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-11-19T11:09:32.593700image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-11-19T11:09:36.143884image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-11-19T11:08:55.962203image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-11-19T11:08:59.219690image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-11-19T11:09:02.388853image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-11-19T11:09:05.914283image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-11-19T11:09:09.400211image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-11-19T11:09:13.061867image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-11-19T11:09:16.676568image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-11-19T11:09:20.167335image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-11-19T11:09:23.198661image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-11-19T11:09:26.402843image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-11-19T11:09:29.722194image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-11-19T11:09:32.805134image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-11-19T11:09:36.420278image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-11-19T11:08:56.284530image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-11-19T11:08:59.421692image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-11-19T11:09:02.621356image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-11-19T11:09:06.191186image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-11-19T11:09:09.670783image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-11-19T11:09:13.317353image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-11-19T11:09:16.908716image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-11-19T11:09:20.407346image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-11-19T11:09:23.433100image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-11-19T11:09:26.645521image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-11-19T11:09:29.966068image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-11-19T11:09:33.018173image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Correlations

2025-11-19T11:09:53.091386image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Datum (Ortszeit)Stunde des Tages (Ortszeit)VollständigkeitZScore_Det0ZScore_Det1ZScore_Det2hist_corhour_localqkfzqlkwqpkwvkfzvlkwvpkw
Datum (Ortszeit)1.0000.0000.0490.0650.0690.0640.1920.0000.0680.0190.0610.0680.0660.068
Stunde des Tages (Ortszeit)0.0001.000-0.000-0.004-0.003-0.002-0.0011.0000.3110.1020.307-0.107-0.106-0.106
Vollständigkeit0.049-0.0001.0000.0040.007-0.0030.128-0.0000.0680.0320.0830.012-0.0350.017
ZScore_Det00.065-0.0040.0041.000-0.0120.0720.157-0.0040.056-0.0220.0680.0380.0280.039
ZScore_Det10.069-0.0030.007-0.0121.0000.0580.158-0.0030.0590.0880.0600.0560.0580.056
ZScore_Det20.064-0.002-0.0030.0720.0581.0000.142-0.0020.0910.0520.1010.0090.0070.011
hist_cor0.192-0.0010.1280.1570.1580.1421.000-0.0010.3930.1840.4090.2960.2280.301
hour_local0.0001.000-0.000-0.004-0.003-0.002-0.0011.0000.3110.1020.307-0.107-0.106-0.106
qkfz0.0680.3110.0680.0560.0590.0910.3930.3111.0000.5690.975-0.082-0.142-0.082
qlkw0.0190.1020.032-0.0220.0880.0520.1840.1020.5691.0000.466-0.050-0.089-0.054
qpkw0.0610.3070.0830.0680.0600.1010.4090.3070.9750.4661.000-0.079-0.138-0.077
vkfz0.068-0.1070.0120.0380.0560.0090.296-0.107-0.082-0.050-0.0791.0000.9640.997
vlkw0.066-0.106-0.0350.0280.0580.0070.228-0.106-0.142-0.089-0.1380.9641.0000.952
vpkw0.068-0.1060.0170.0390.0560.0110.301-0.106-0.082-0.054-0.0770.9970.9521.000

Missing values

2025-11-19T11:09:36.838388image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
A simple visualization of nullity by column.
2025-11-19T11:09:38.132851image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-11-19T11:09:40.556628image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

Datum (Ortszeit)Stunde des Tages (Ortszeit)VollständigkeitZScore_Det0ZScore_Det1ZScore_Det2hist_corlocalTimemonthqkfzqlkwqpkwutcvkfzvlkwvpkwdetector_iddate_localhour_local
02024-01-010NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN2023-12-31 23:00:00+00:00NaNNaNNaNTEU00002_Det02024-01-010
12024-01-011NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN2024-01-01 00:00:00+00:00NaNNaNNaNTEU00002_Det02024-01-011
22024-01-012NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN2024-01-01 01:00:00+00:00NaNNaNNaNTEU00002_Det02024-01-012
32024-01-013NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN2024-01-01 02:00:00+00:00NaNNaNNaNTEU00002_Det02024-01-013
42024-01-014NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN2024-01-01 03:00:00+00:00NaNNaNNaNTEU00002_Det02024-01-014
52024-01-015NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN2024-01-01 04:00:00+00:00NaNNaNNaNTEU00002_Det02024-01-015
62024-01-016NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN2024-01-01 05:00:00+00:00NaNNaNNaNTEU00002_Det02024-01-016
72024-01-017NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN2024-01-01 06:00:00+00:00NaNNaNNaNTEU00002_Det02024-01-017
82024-01-018NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN2024-01-01 07:00:00+00:00NaNNaNNaNTEU00002_Det02024-01-018
92024-01-019NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN2024-01-01 08:00:00+00:00NaNNaNNaNTEU00002_Det02024-01-019
Datum (Ortszeit)Stunde des Tages (Ortszeit)VollständigkeitZScore_Det0ZScore_Det1ZScore_Det2hist_corlocalTimemonthqkfzqlkwqpkwutcvkfzvlkwvpkwdetector_iddate_localhour_local
5440152024-01-3113NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN2024-01-31 12:00:00+00:00NaNNaNNaNteuscalaS00000DD00361D02024-01-3113
5440162024-01-3114NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN2024-01-31 13:00:00+00:00NaNNaNNaNteuscalaS00000DD00361D02024-01-3114
5440172024-01-3115NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN2024-01-31 14:00:00+00:00NaNNaNNaNteuscalaS00000DD00361D02024-01-3115
5440182024-01-3116NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN2024-01-31 15:00:00+00:00NaNNaNNaNteuscalaS00000DD00361D02024-01-3116
5440192024-01-3117NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN2024-01-31 16:00:00+00:00NaNNaNNaNteuscalaS00000DD00361D02024-01-3117
5440202024-01-3118NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN2024-01-31 17:00:00+00:00NaNNaNNaNteuscalaS00000DD00361D02024-01-3118
5440212024-01-3119NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN2024-01-31 18:00:00+00:00NaNNaNNaNteuscalaS00000DD00361D02024-01-3119
5440222024-01-3120NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN2024-01-31 19:00:00+00:00NaNNaNNaNteuscalaS00000DD00361D02024-01-3120
5440232024-01-3121NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN2024-01-31 20:00:00+00:00NaNNaNNaNteuscalaS00000DD00361D02024-01-3121
5440242024-01-3122NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN2024-01-31 21:00:00+00:00NaNNaNNaNteuscalaS00000DD00361D02024-01-3122